3 Key Metrics to Measure Developer Productivity
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Organizations are understandably excited about the potential for AI coding assistants to increase the productivity of software developers. In a recent survey by GitLab, 83% of respondents said it is essential to implement AI in their software development processes to avoid falling behind.
The promise that AI can automate most software development or at least make developers significantly more productive raises the question of how developer productivity is measured. Hint: It’s not about code volume.
While most measurements of developer productivity focus on task metrics such as lines of code produced or the number of code commits, developer productivity can’t be measured in a single dimension. Multiple dimensions of developer performance must be considered to accurately measure developer productivity.
The task dimension covers the most direct measure of individual developer output — efficiently completing an assigned task within a set timeframe. While seemingly straightforward, measuring task completion as an indication of productivity can be misguided — for two reasons.
